Multivariate Statistical Analysis

Written by Tara Peris
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Multivariate statistical analysis is at once both a time-tested standby in the field of statistics, and a modern, cutting edge technique that is constantly evolving to address new mathematical issues. It is easy to learn, both from a conceptual and an applied standpoint, and it can be used in fields ranging from engineering and business to psychology and the social sciences. Consequently, it is well worth taking the time to learn the basics of multivariate analysis.

Multivariate statistical analysis encompasses a range of procedures that may be used to examine multiple outcomes. Common techniques include canonical correlation and factor analysis, analytic techniques that have a wide range of purposes. Most recently, multivariate analysis has gained popularity in business and engineering circles, as its strengths have become evident in the realm of product development via DOE and DFSS strategies.

Learning Multivariate Statistical Analysis
Much of the analysis that goes into product development centers on predicting different outcomes and examining how different model parameters contribute to fluctuations in outcome. This typically falls within the domain of multivariate statistical analysis, where old-school matrices have been replaced by highly efficient and user-friendly software. With modern software packages and a good stats textbook, it is easier than ever to gain familiarity with the field.

Indeed, many modern statistical textbooks contain statistical syntax that is annotated so as to allow it to be applied to new projects with ease. Developments such as these make it remarkably easy to learn a statistical approach that has been around for ages, and to apply it to modern day analytical issues. If you aren't familiar with the field, take some time to explore how it may inform your next project.

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